4,492 research outputs found
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion
Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows
Counting Crowds in Bad Weather
Crowd counting has recently attracted significant attention in the field of
computer vision due to its wide applications to image understanding. Numerous
methods have been proposed and achieved state-of-the-art performance for
real-world tasks. However, existing approaches do not perform well under
adverse weather such as haze, rain, and snow since the visual appearances of
crowds in such scenes are drastically different from those images in clear
weather of typical datasets. In this paper, we propose a method for robust
crowd counting in adverse weather scenarios. Instead of using a two-stage
approach that involves image restoration and crowd counting modules, our model
learns effective features and adaptive queries to account for large appearance
variations. With these weather queries, the proposed model can learn the
weather information according to the degradation of the input image and
optimize with the crowd counting module simultaneously. Experimental results
show that the proposed algorithm is effective in counting crowds under
different weather types on benchmark datasets. The source code and trained
models will be made available to the public.Comment: including supplemental materia
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go
Adverse conditions typically suffer from stochastic hybrid weather
degradations (e.g., rainy and hazy night), while existing image restoration
algorithms envisage that weather degradations occur independently, thus may
fail to handle real-world complicated scenarios. Besides, supervised training
is not feasible due to the lack of a comprehensive paired dataset to
characterize hybrid conditions. To this end, we have advanced the
aforementioned limitations with two tactics: framework and data. First, we
present a novel unified framework, dubbed RAHC, to Restore Arbitrary Hybrid
adverse weather Conditions in one go. Specifically, our RAHC leverages a
multi-head aggregation architecture to learn multiple degradation
representation subspaces and then constrains the network to flexibly handle
multiple hybrid adverse weather in a unified paradigm through a discrimination
mechanism in the output space. Furthermore, we devise a reconstruction vectors
aided scheme to provide auxiliary visual content cues for reconstruction, thus
can comfortably cope with hybrid scenarios with insufficient remaining image
constituents. Second, we construct a new dataset, termed HAC, for learning and
benchmarking arbitrary Hybrid Adverse Conditions restoration. HAC contains 31
scenarios composed of an arbitrary combination of five common weather, with a
total of ~316K adverse-weather/clean pairs. Extensive experiments yield
superior results and establish new state-of-the-art results on both HAC and
conventional datasets.Comment: In submissio
Non-implementation of property rating practice, any impact on community healthcare in Bauchi Metropolis Nigeria?
The practice of rating real estate is essentially an internal revenue source, synonymous to tenement tax levied on the owner/occupier. Property rating in Nigeria is bedevilled by many factors that impeded its smooth implementation and operation, thus, this form of taxation yields zero revenue in Bauchi, due to failure of implementation. This study is aimed at measuring the impact of non-implementation of property rating on community healthcare in Bauchi metropolis of Nigeria. Two hundred and fifty (250) closed-ended questionnaires composed in five-level Likert scale were distributed to professionals in the field of real estate and facilities management, in the academia and estate firms, and two hundred and twenty one questionnaires (221) were mailed back for analysis. The Structural Equation Modelling (SEM) in IBM version of SPSS with AMOS was used to establish relationship between the variables. Findings from this study reveals that PRP does not command direct impact on community healthcare services, however, the services financed by property rating in the area of sanitation and sewage cleaning has the tendencies to curb the occurrence of diseases like cholera and malaria. Thus, it can be understood that a fully institutionalized practice of property rating could avert the outbreak of diseases
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